Event Studies for Publicly Traded Insurers: An Investigation of the Bad-Model Problem

Abstract

The potential that abnormal returns are due to a misspecified expected (normal) return model is well known in the event study literature. Prior research shows that this “bad-model problem” is serious in long-run studies, and can also be problematic in short-run studies for firms grouped by certain characteristics. We investigate the bad-model problem for a large sample of insurance firms over an 18-year period, based on nine different expected return models and short- and long-run event windows. Using 1000 samples of randomly selected firms and dates, we find that the different normal return models make little difference in the statistical or economic significance of abnormal returns for short event windows (up to 3 days). However, for longer event windows, such as 1 month and 13 months, statistically and economically significant abnormal returns are more common. Further, we find that characteristic-based benchmark models generally perform better than models that require an estimation period. We also examine a sample of insurers that experienced a financial strength rating downgrade, and find significant differences between characteristic-based benchmark models and other normal return models for the 13-month event window. We recommend that abnormal returns from actual events be evaluated for their qualitative significance in relation to random samples with random event dates. Our results support the need to exercise caution in interpreting the findings of event studies.